WO2022156610A1 - 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 - Google Patents

基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 Download PDF

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WO2022156610A1
WO2022156610A1 PCT/CN2022/072196 CN2022072196W WO2022156610A1 WO 2022156610 A1 WO2022156610 A1 WO 2022156610A1 CN 2022072196 W CN2022072196 W CN 2022072196W WO 2022156610 A1 WO2022156610 A1 WO 2022156610A1
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gene
liver cancer
gene expression
index
aerobic glycolysis
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徐俊杰
蔡秀军
潘宇
梁霄
夏顺杰
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浙江大学
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  • the invention belongs to the fields of biotechnology and medicine, and in particular, the invention relates to gene detection related to anti-tumor drug resistance and its application.
  • Hepatocellular carcinoma is the sixth most common malignant tumor in my country and worldwide, and ranks fourth among tumor-related causes of death. Despite advances in treatment, the five-year survival rate for liver cancer is still between 25% and 55%. Distant metastasis, intrahepatic recurrence and low sensitivity to various therapies are the main reasons for the poor prognosis of liver cancer. Gene mutations, chromosomal abnormalities, and abnormal cell signaling pathways are closely related to the occurrence and development of liver cancer. The classification of liver cancer by molecular biological characteristics can help achieve precise treatment and improve the prognosis of liver cancer patients.
  • Aerobic glycolysis is a hallmark feature of tumor malignancy, which mainly means that even when the oxygen concentration is at a physiological concentration, tumor cells still obtain a large amount of energy through glycolysis. Through this change in glucose metabolism, tumor cells can quickly obtain energy while producing a large number of metabolites required for physiological synthesis.
  • aerobic glycolysis is closely related to a variety of oncogene signaling pathways. Therefore, classifying liver cancer by the level of aerobic glycolysis may reveal new molecular typing of liver cancer.
  • Sorafenib is currently the first-line treatment for advanced liver cancer. But sorafenib resistance is very common in clinical practice. How to screen out patients who are sensitive to sorafenib therapy and precise drug use are crucial to improving the prognosis of patients with liver cancer. Tumor metabolism, tumor microenvironment changes, and epigenetics are also considered to be related to sorafenib resistance in liver cancer. However, the dominant mechanism or key gene is still the main problem that plagues the research of sorafenib resistance in liver cancer.
  • the purpose of the present invention is to find a new predictive tool for predicting the sensitivity and long-term prognosis of liver cancer to sorafenib against the deficiencies of the prior art.
  • aerobic glycolysis index LDHA gene expression * 0.163+ STC2 gene expression amount*0.004+GPC1 gene expression amount*0.034+TKTL1 gene expression amount*0.0001+SLC2A1 gene expression amount*0.014+SRD5A3 gene expression amount*0.032+PLOD2 gene expression amount*0.070+G6PD gene expression amount*0.083+HMMR Gene expression*0.040+HOMER1 gene expression*0.001+RARS1 gene expression*0.132-GOT2 gene expression*0.146+CENPA gene expression*0.053-SLC2A2 gene expression*0.001.
  • the detection method/technology of gene expression level includes: second-generation RNA sequencing or third-generation RNA sequencing or gene chip technology.
  • the index was verified in multiple public databases and clinical samples from Run Run Shaw Hospital, and it was found that the index can accurately predict the long-term prognosis of liver cancer patients; the "survminer" data package was used to obtain the corresponding detection of the data set according to the survival data of the data set
  • the method corresponds to the optimal threshold of aerobic glycolysis index. If the patient's aerobic glycolysis index is higher than the threshold, it indicates that the prognosis of liver cancer patients is poor; otherwise, it indicates that the prognosis of liver cancer patients is good.
  • the index was verified in GDSC and CCLE databases and clinical samples of the "STORM” trial, and it was found that the index was negatively correlated with sorafenib sensitivity, and could accurately predict the sensitivity of patients to sorafenib therapy.
  • the optimal threshold of the aerobic glycolysis index corresponding to the corresponding detection method of the data set is obtained. If the patient's aerobic glycolysis index is higher than the threshold, the patient is instructed to The sensitivity of fini therapy is poor, and vice versa, it indicates that the patient has good sensitivity to sorafenib therapy.
  • the present invention also provides a kit for judging the drug sensitivity and long-term prognosis of liver cancer based on gene detection, which comprises a kit for measuring LDHA gene, STC2 gene, GPC1 gene, TKTL1 gene, SLC2A1 gene, SRD5A3 gene, PLOD2 gene and G6PD gene , HMMR gene, HOMER1 gene, RARS1 gene, GOT2 gene, CENPA gene, SLC2A2 gene expression reagents.
  • the reagent is a primer or probe that specifically binds to the gene.
  • the index of the invention is only based on the expression levels of 14 genes, the method is simple, the prediction accuracy is high, the promotion is easy, and it has very good clinical transformation value.
  • Figure 1 shows that 80 aerobic glycolysis-related genes are associated with the prognosis of liver cancer by univariate Cox analysis
  • Figure 2 shows the simplified prognosis-related genes by LASSO regression analysis, and established an aerobic glycolysis index based on the expression of 14 genes;
  • Figure 3 is a graph showing aerobic glycolysis index predicting the overall survival (a) and tumor-free survival (b) of liver cancer patients in the TCGA database; in the figure, 2 represents the survival curve of low AGI, 1 and 3 are respectively The error bars of the survival curve of low AGI; 5 represents the survival curve of high AGI, and 4 and 6 are the error bars of the survival curve of high AGI, respectively.
  • Fig. 4 is the ROC curve diagram of TCGA-LIHC data
  • Figure 5 is a graph of the overall survival rate of aerobic glycolysis index predicting GSE14520 (a) and LIRI-JP database (b) and the liver cancer patients in Shaw Hospital (c); in the figure, 2 indicates the survival curve of low AGI , 1, 3 are the error bars of the survival curve of low AGI; 5 is the survival curve of high AGI, 4, 6 are the error bars of the survival curve of high AGI, respectively.
  • Figure 6 is a graph showing the negative correlation between the sensitivity of hepatoma cell lines to sorafenib and the aerobic glycolysis index in GDSC (a) and CCLE database (b);
  • Figure 7 is "STORM" clinical data showing that aerobic glycolysis index can predict response to sorafenib therapy.
  • Figure 8 is an AUC plot of "STORM" clinical data.
  • TCGA-LIHC data is downloaded from UCSC database (https://xenabrowser.net/datapages), LIRI-JP data is downloaded from HCCDB database ( http://lifeome.net/database/hccdb/download.html ).
  • GSE14520 and GSE109211 data Downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo/). Sorafenib sensitivity data of liver cancer cell lines downloaded from GDSC database (https://www.cancerrxgene.org) and The CCLE database ( https://portals.broadinstitute.org/ccle/data ).
  • the data of Run Run Run Shaw Hospital is derived from the 102 cases of hospital visits in the Shaw Hospital affiliated to Zhejiang University School of Medicine from January 2008 to January 2018.
  • the 102 cases were All the cases were diagnosed with liver cancer, TNM stage was I-IV, T stage was T1-T4, the age was 32-88 years old, and the clinical follow-up time was more than 2 years.
  • AGI aerobic glycolysis index
  • Oxygen glycolysis index according to the patient survival data, use the R language software "survminer" data package, take the best threshold of 4.05, the aerobic glycolysis index is lower than 4.05, the low aerobic glycolysis index group is low aerobic glycolysis In the hydrolysis index group (low AGI group), the aerobic glycolysis index higher than 4.05 is the high aerobic glycolysis index group (high AGI group).
  • the Kaplan-Meier survival curve and log-rank survival analysis showed that the aerobic glycolysis index indicated that the liver cancer patients in the high AGI group had a worse long-term prognosis, including the overall survival rate and tumor-free survival rate, as shown in Figure 3 .
  • the ROC curve was used to evaluate the clinical accuracy of the model in this example.
  • the ROC curve is shown in Figure 4.
  • the abscissa is the 1-specificity
  • the ordinate is the sensitivity
  • the specificity was 0.65
  • the sensitivity was 0.69
  • the AUC value of the calculation model was 0.714, indicating that the model prediction results were more accurate.
  • the area under the ROC curve is between 1.0 and 0.5. When the AUC is greater than 0.5, the closer the AUC is to 1, the better the diagnostic effect.
  • COX regression analysis was used to verify the related risk factors of aerobic glycolysis index on the long-term prognosis of liver cancer patients in TCGA.
  • Control female tumor differentiation degree (G3, G2, control G1), tumor stage (IV, III, II, control I), vascular invasion (macrovascular invasion, micro-invasion, control without invasion)
  • Other clinical indicators are not independent risk factors for the long-term prognosis of liver cancer patients
  • the aerobic glycolysis index is an independent risk factor for the long-term prognosis of liver cancer patients, as shown in Figure 5.
  • the results indicate that the aerobic glycolysis index of the present invention can independently predict the long-term prognosis of liver cancer patients, and is not affected by clinical indicators such as age, gender, tumor differentiation, tumor stage, and vascular invasion.
  • liver cancer patients in the GSE14520 database 200 liver cancer patients in the LIRI-JP database, and 102 liver cancer patients in Run Run Run Run Shaw Hospital.
  • Affymetrix Human Genome U133A 2.0 Array GSE14520
  • Illumina RNA-Seq LIRI-JP
  • Illumina Ren Run Shaw Hospital
  • the aerobic glycolysis index is lower than the optimal threshold as the low AGI group, and the AGI higher than the optimal threshold is High AGI group.
  • the aerobic glycolysis index suggested a worse overall survival rate of liver cancer patients in the high AGI group, as shown in Figure 6.
  • the aerobic glycolysis index could effectively predict the sensitivity of HCC patients to sorafenib, and the area under the curve was 0.879, as shown in Figure 8.
  • the threshold value of 3.488 corresponds to a sensitivity of 0.905 and a specificity of 0.848.
  • the present embodiment also provides a method for predicting the sensitivity of a patient to sorafenib therapy using the present invention, which specifically includes the following steps:
  • tissue samples such as surgical specimens, puncture specimens, etc.
  • the aerobic glycolysis index level of the tested sample is lower than the threshold, the patient has a good prognosis and is sensitive to sorafenib therapy, and sorafenib adjuvant therapy can be performed to improve the prognosis. Conversely, if the aerobic glycolysis index level of the tested sample is higher than the threshold, the patient has a poor prognosis, is not sensitive to sorafenib therapy, and is not suitable for sorafenib adjuvant therapy.

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Abstract

提供一种基于基因检测判断肝癌药物敏感性和远期预后的预测工具，通过统计分析TCGA数据中的与肝癌预后相关有氧糖酵解通路基因，在此基础上采用LASSO回归分析简化预后相关基因，建立基于有氧糖酵解通路基因的预测工具，简称有氧糖酵解指数。将该指数于多个公共数据库及邵逸夫医院临床样本中验证，发现该指数可准确预测肝癌患者对索拉菲尼疗法的敏感和远期预后。可以有效的筛选对索拉菲尼疗法敏感的肝细胞肝癌患者，为肝癌患者精准、综合治疗提供新思路。

Description

基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 技术领域
本发明属于生物技术和医学领域，具体地，本发明涉及抗肿瘤耐药相关的基因检测及其应用。
背景技术
肝癌是我国也是世界范围内第六常见的恶性肿瘤，同时在肿瘤相关死因中排名第四位。尽管治疗方法上取得了长足发展，但肝癌的五年生存率仍在25％～55％之间。远处转移、肝内复发以及对各种疗法敏感性不高是肝癌预后差的主要原因。基因突变、染色体异常、细胞信号通路异常与肝癌发生、发展密切相关。通过分子生物学特点对肝癌进行分型有助于实现精准治疗，改善肝癌患者预后。
有氧糖酵解是肿瘤恶性程度的一大标志性特征，其主要是指即使在氧浓度处于生理浓度时，肿瘤细胞仍然大量通过糖酵解的途径获得能量。通过这种糖代谢模式的改变，肿瘤细胞在快速获得能量的同时，还产生了大量生理合成所需的代谢产物。此外有氧糖酵解与多种癌基因信号通路密切相关。因此，通过有氧糖酵解水平将肝癌进行可能揭示新的肝癌分子分型。
索拉菲尼目前是晚期肝癌的一线治疗药物。但是索拉菲尼耐药现象在临床中非常常见。如何筛选出对索拉菲尼疗法敏感的患者，精准用药，对改善肝癌患者预后至关重要。肿瘤代谢、肿瘤微环境改变、表观遗传学等也被认为可能与肝癌索拉非尼耐药相关。但主导机制或者说关键基因仍然是目前困扰肝癌对索拉非尼耐药研究的主要难题。
因此，本领域迫切需要寻找能够预测肝癌对索拉菲尼敏感性和肝癌远期预后的新方法，通过此实现精准治疗，改善患者预后。
发明内容
本发明的目的是针对现有技术的不足，寻找一种新的预测肝癌对索拉菲尼敏感性和远期预后的预测工具。
本发明是通过以下技术方案来实现的：
1.通过使用单因素Cox回归模型统计筛选TCGA数据中与肝癌预后相关有氧糖酵解通路基因；
2.在此基础上采用LASSO回归分析简化预后相关基因，建立基于有氧糖酵解通路基因的预测工具，简称有氧糖酵解指数；有氧糖酵解指数＝LDHA基因表达量*0.163+STC2基因表达 量*0.004+GPC1基因表达量*0.034+TKTL1基因表达量*0.0001+SLC2A1基因表达量*0.014+SRD5A3基因表达量*0.032+PLOD2基因表达量*0.070+G6PD基因表达量*0.083+HMMR基因表达量*0.040+HOMER1基因表达量*0.001+RARS1基因表达量*0.132-GOT2基因表达量*0.146+CENPA基因表达量*0.053-SLC2A2基因表达量*0.001。
其中，基因表达量的检测方法/技术包括：二代RNA测序或三代RNA测序或基因芯片技术。
3.将该指数于多个公共数据库及邵逸夫医院临床样本中验证，发现该指数可准确预测肝癌患者远期预后；利用“survminer”数据包，根据数据集的生存资料，获取数据集相应检测方法对应的有氧糖酵解指数最佳阈值，若患者有氧糖酵解指数高于阈值，则指示肝癌患者预后差，反之，则指示肝癌患者预后好。
4.将该指数于GDSC和CCLE数据库及“STORM”试验临床样本中验证，发现该指数与索拉菲尼敏感性负相关，并可准确预测患者对索拉菲尼疗法的敏感。利用“survminer”数据包，根据数据集的生存资料，获取数据集相应检测方法对应的有氧糖酵解指数最佳阈值，若患者有氧糖酵解指数高于阈值，则指示患者对索拉菲尼疗法敏感性差，反之，则指示患者对索拉菲尼疗法敏感性好。
本发明还提供了一种基于基因检测判断肝癌药物敏感性和远期预后的试剂盒，包含用于测量LDHA基因、STC2基因、GPC1基因、TKTL1基因、SLC2A1基因、SRD5A3基因、PLOD2基因、G6PD基因、HMMR基因、HOMER1基因、RARS1基因、GOT2基因、CENPA基因、SLC2A2基因表达量的试剂。
优选地，所述的试剂为与所述基因特异性结合的引物或探针。
本发明的有益效果是：本发明的指数仅基于14个基因表达水平，方法简单，预测准确性高，易于推广，具有非常好的临床转化价值。
附图说明
下面结合附图和实施例对本发明进一步说明；
图1为单因素Cox分析提示80个有氧糖酵解相关基因与肝癌预后相关；
图2为LASSO回归分析简化预后相关基因，建立了基于14个基因表达量的有氧糖酵解指数；
图3是有氧糖酵解指数可预测TCGA数据库中肝癌病人的总体生存率(a)和无瘤生存率(b)曲线图；图中，2表示低AGI的生存曲线，1、3分别是低AGI的生存曲线的误差线；5表示高AGI的生存曲线，4、6分别是高AGI的生存曲线的误差线。
图4是TCGA-LIHC数据的ROC曲线图；
图5是有氧糖酵解指数可预测GSE14520(a)和LIRI-JP数据库(b)及邵逸夫医院中肝癌病人(c)的总体生存率曲线图；图中，2表示低AGI的生存曲线，1、3分别是低AGI的生存曲线的误差线；5表示高AGI的生存曲线，4、6分别是高AGI的生存曲线的误差线。
图6是GDSC(a)和CCLE数据库(b)中肝癌细胞系对索拉菲尼敏感性与有氧糖酵解指数负相关的曲线图；
图7是“STORM”临床数据显示有氧糖酵解指数可以预测对索拉菲尼疗法反应。
图8是“STORM”临床数据的AUC曲线图。
具体实施方式
下面通过实验并结合实例对本发明做进一步说明，应该理解的是，这些实施例仅用于例证的目的，决不限制本发明的保护范围。
本实施例所涉及测序和临床数据及试剂来源：
TCGA-LIHC数据下载于UCSC数据库(https://xenabrowser.net/datapages),LIRI-JP数据下载于HCCDB数据库( http://lifeome.net/database/hccdb/download.html).GSE14520和GSE109211数据下载于GEO数据库(https://www.ncbi.nlm.nih.gov/geo/).肝癌细胞系对索拉菲尼敏感性数据下载于GDSC数据库(https://www.cancerrxgene.org)和CCLE数据库( https://portals.broadinstitute.org/ccle/data).邵逸夫医院数据源自浙江大学医学院附属邵逸夫医院2008年1月至2018年1月的102例就诊病例，该102例就诊病例中，均确诊为肝癌，TNM分期为Ⅰ-Ⅳ，T分期为T1-T4，年龄为32-88岁，临床随访时间大于2年。
实施例：
选取TCGA-LIHC数据中371例肝癌病人测序数据和临床随访信息，通过单因素COX回归分析有氧糖酵解基因对该371例患者总体生存率影响。结果显示，共有80个基因显著影响肝癌患者总体生存率，如图1所示。
通过LASSO回归分析，将简化预后相关基因，建立了基于14个基因表达量的有氧糖酵解指数，如图2所示，赋值具体为有氧糖酵解指数(AGI)＝LDHA基因表达量*0.163+STC2基因表达量*0.004+GPC1基因表达量*0.034+TKTL1基因表达量*0.0001+SLC2A1基因表达量*0.014+SRD5A3基因表达量*0.032+PLOD2基因表达量*0.070+G6PD基因表达量*0.083+HMMR基因表达量*0.040+HOMER1基因表达量*0.001+RARS1基因表达量*0.132-GOT2基因表达量*0.146+CENPA基因表达量*0.053-SLC2A2基因表达量*0.001。
基于有氧糖酵解指数(AGI)可以对病例进行分组，其中分组的阈值是所分开的两组病人预后差异最大的点，如根据病人生存资料，使用R语言软件“survminer”数据包获取最佳阈值，需要指出的是，针对不同的测序方法，阈值会有所不同。下面结合具体的验证集进行详细说明：
在TCGA-LIHC数据中验证有氧糖酵解指数对肝癌患者远期预后的影响，即通过Illumina HiSeq 2000 RNA测序平台，检测病人肝癌组织各基因表达水平，经标准化处理后，计算各个肝癌病人有氧糖酵解指数，根据病人生存资料，使用R语言软件“survminer”数据包，取最佳阈值4.05，有氧糖酵解指数低于4.05为低有氧糖酵解指数组低有氧糖酵解指数组(低AGI组)，有氧糖酵解指数高于4.05为高有氧糖酵解指数组(高AGI组)。其中，通过Kaplan-Meier生存曲线和log-rank生存分析发现有氧糖酵解指数提示高AGI组的肝癌患者更差的远期预后，包括总体生存率和无瘤生存率，如图3所示。
同时应用ROC曲线图评价本实施例模型的临床准确性，ROC曲线图见图4，横坐标是1-特异度，纵坐标是灵敏度，五年生存率为节点的情况下，当取4.05时，其特异度为0.65，敏感性为0.69，计算模型的AUC值为0.714，说明模型预测结果准确性较高。ROC曲线下的面积值在1.0和0.5之间，在AUC大于0.5的情况下，AUC越接近于1，说明诊断效果越好。
进一步地，采用COX回归分析在TCGA中验证有氧糖酵解指数对肝癌患者远期预后的相关危险因素，多因素回归分析发现年龄(大于等于60岁，对照小于60岁)、性别(男，对照女)、肿瘤分化程度(G3级、G2级，对照G1级)、肿瘤分期(IV期、III期、II期，对照I期)、血管侵犯(大血管浸润、微浸润，对照无浸润)等临床指标均不是肝癌患者远期预后的独立危险因素，有氧糖酵解指数是肝癌患者远期预后的独立危险因素，如图5所示。该结果说明，利用本发明的有氧糖酵解指数能够独立地预测肝癌患者的远期预后，且不受年龄、性别、肿瘤分化程度、肿瘤分期、血管侵犯等临床指标的影响。
在GSE14520数据库243例肝癌患者、LIRI-JP数据库中200例肝癌患者、和邵逸夫医院102肝癌患者中进一步验证有氧糖酵解指数对肝癌患者远期预后的影响，同样地，分别通过Affymetrix Human Genome U133A 2.0 Array(GSE14520)、Illumina RNA-Seq(LIRI-JP)、Illumina(邵逸夫医院)测序平台，检测病人肝癌组织各基因表达水平，经标准化处理后，计算各个肝癌病人有氧糖酵解指数并取最佳阈值(3.245(GSE14520)、1.785(LIRI-JP)、1.64(邵逸夫医院))，有氧糖酵解指数低于最佳阈值为低AGI组，AGI高于最佳阈值为高AGI组。有氧糖酵解指数提示高AGI组的肝癌患者更差的总体生存率，如图6所示。
在GDSC数据库中提示肝癌细胞系对索拉菲尼IC50浓度与有氧糖酵解指数成正相关。在CCLE数据库中提示肝癌细胞系对索拉菲尼EC50浓度与有氧糖酵解指数成正相关，如图7a、7b所示。
在“STORM”数据库67例接受索拉菲尼治疗的肝癌患者中，有氧糖酵解指数可有效预测肝癌患者对索拉菲尼的敏感性，曲线下面积为0.879，如图8所示。其中，阈值3.488对应的敏感性为0.905、特异度为0.848。
本实施例还提供一种利用本发明预测患者对索拉菲尼疗法的敏感性方法，具体包括如下步骤：
1.收取肝癌患者组织样本(如手术标本、穿刺标本等)，提取组织中总RNA。
2.选取合适的测序平台检测有氧糖酵解指数相关基因，计算有氧糖酵解指数。
3.根据已经建立的有氧糖酵解指数数据库，通过参照数据库中有氧糖酵解指数最佳阈值，判断样本的有氧糖酵解指数水平。
4.若被检样本有氧糖酵解指数水平低于阈值，则该病人预后较好，对索拉菲尼疗法敏感，可进行索拉菲尼辅助治疗，改善预后。反之，若被检样本有氧糖酵解指数水平高于阈值，则该病人预后较差，对索拉菲尼疗法不敏感，不适合索拉菲尼辅助治疗。

Claims (5)

  1. 一种基因检测试剂在制备判断患者对肝癌药物索拉非尼敏感性试剂盒中的应用，其中所述基因检测试剂为测量LDHA基因、STC2基因、GPC1基因、TKTL1基因、SLC2A1基因、SRD5A3基因、PLOD2基因、G6PD基因、HMMR基因、HOMER1基因、RARS1基因、GOT2基因、CENPA基因和SLC2A2基因表达量的试剂。
  2. 根据权利要求1所述的应用，其特征在于，试剂为与所述基因特异性结合的引物或探针。
  3. 一种基因检测试剂在制备肝癌患者远期预后预测试剂盒中的应用，其中所述基因检测试剂为测量LDHA基因、STC2基因、GPC1基因、TKTL1基因、SLC2A1基因、SRD5A3基因、PLOD2基因、G6PD基因、HMMR基因、HOMER1基因、RARS1基因、GOT2基因、CENPA基因和SLC2A2基因表达量的试剂。
  4. 根据权利要求3所述的应用，其特征在于，试剂为与所述基因特异性结合的引物或探针。
  5. 一种构建预测肝癌患者对索拉菲尼敏感性和远期预后的预测工具的方法，其特征在于，具体为：
    (1)通过使用单因素Cox回归模型统计筛选TCGA数据中与肝癌预后相关有氧糖酵解通路基因；
    (2)在此基础上采用LASSO回归分析简化预后相关基因，建立基于有氧糖酵解通路基因的预测工具，简称有氧糖酵解指数；有氧糖酵解指数＝LDHA基因表达量*0.163+STC2基因表达量*0.004+GPC1基因表达量*0.034+TKTL1基因表达量*0.0001+SLC2A1基因表达量*0.014+SRD5A3基因表达量*0.032+PLOD2基因表达量*0.070+G6PD基因表达量*0.083+HMMR基因表达量*0.040+HOMER1基因表达量*0.001+RARS1基因表达量*0.132-GOT2基因表达量*0.146+CENPA基因表达量*0.053-SLC2A2基因表达量*0.001；
    其中，基因表达量的检测技术包括：二代RNA测序或三代RNA测序或基因芯片技术。
PCT/CN2022/072196 2021-01-21 2022-01-15 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 WO2022156610A1 (zh)

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